Jiangxi University of Science and Technology

Industry 4.0 (the fourth industrial revolution) encapsulates future industry development trends to achieve more intelligent manufacturing processes, including reliance on Cyber-Physical Systems (CPS), construction of Cyber-Physical Production Systems (CPPS), and implementation and operation of smart factories. This paper introduces relevant aspects of Industry 4.0 in relation to strategic planning, key technologies, opportunities, and challenges. Strategic planning includes construction of a CPS network, discussion of two major themes which are based on the smart factory and intelligent production, achieving three integrations (horizontal integration, vertical integration and end-to-end integration) and achieving eight plans which consist of the formulation of system standardization, efficient management etc. Finally, it referred to the enlightenment for China's manufacturing industries, to build China's Industry 4.0.

Due to the current structure of digital factory, it is necessary to build the smart factory to upgrade the manufacturing industry. Smart factory adopts the combination of physical technology and cyber technology and deeply integrates previously independent discrete systems making the involved technologies more complex and precise than they are now. In this paper, a hierarchical architecture of the smart factory was proposed first, and then the key technologies were analyzed from the aspects of the physical resource layer, the network layer, and the data application layer. In addition, we discussed the major issues and potential solutions to key emerging technologies, such as Internet of Things (IoT), big data, and cloud computing, which are embedded in the manufacturing process. Finally, a candy packing line was used to verify the key technologies of smart factory, which showed that the overall equipment effectiveness of the equipment is significantly improved.

Bone biomaterials play a vital role in bone repair by providing the necessary substrate for cell adhesion, proliferation, and differentiation and by modulating cell activity and function. In past decades, extensive efforts have been devoted to developing bone biomaterials with a focus on the following issues: (1) developing ideal biomaterials with a combination of suitable biological and mechanical properties; (2) constructing a cell microenvironment with pores ranging in size from nanoscale to submicro- and microscale; and (3) inducing the oriented differentiation of stem cells for artificial-to-biological transformation. Here we present a comprehensive review of the state of the art of bone biomaterials and their interactions with stem cells. Typical bone biomaterials that have been developed, including bioactive ceramics, biodegradable polymers, and biodegradable metals, are reviewed, with an emphasis on their characteristics and applications. The necessary porous structure of bone biomaterials for the cell microenvironment is discussed, along with the corresponding fabrication methods. Additionally, the promising seed stem cells for bone repair are summarized, and their interaction mechanisms with bone biomaterials are discussed in detail. Special attention has been paid to the signaling pathways involved in the focal adhesion and osteogenic differentiation of stem cells on bone biomaterials. Finally, achievements regarding bone biomaterials are summarized, and future research directions are proposed.

In modern society, air pollution is an important topic as this pollution exerts a critically bad influence on human health and the environment. Among air pollutants, Particulate Matter (PM2.5) consists of suspended particles with a diameter equal to or less than 2.5 μm. Sources of PM2.5 can be coal-fired power generation, smoke, or dusts. These suspended particles in the air can damage the respiratory and cardiovascular systems of the human body, which may further lead to other diseases such as asthma, lung cancer, or cardiovascular diseases. To monitor and estimate the PM2.5 concentration, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined and applied to the PM2.5 forecasting system. To compare the overall performance of each algorithm, four measurement indexes, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) Pearson correlation coefficient and Index of Agreement (IA) are applied to the experiments in this paper. Compared with other machine learning methods, the experimental results showed that the forecasting accuracy of the proposed CNN-LSTM model (APNet) is verified to be the highest in this paper. For the CNN-LSTM model, its feasibility and practicability to forecast the PM2.5 concentration are also verified in this paper. The main contribution of this paper is to develop a deep neural network model that integrates the CNN and LSTM architectures, and through historical data such as cumulated hours of rain, cumulated wind speed and PM2.5 concentration. In the future, this study can also be applied to the prevention and control of PM2.5.

Abstract 2D transition metal carbides and/or nitrides (MXenes), by virtue of high electrical conductivity, abundant surface functional groups and excellent dispersion in various solvents, are attracting increasing attention and showing competitive performance in energy storage and conversion applications. However, like other 2D materials, MXene nanosheets incline to stack together via van der Waals interactions, which lead to limited number of active sites, sluggish ionic kinetics, and finally ordinary performance of MXene materials/devices. Constructing 2D MXene nanosheets into 3D architectures has been proven to be an effective strategy to reduce restacking, thus providing larger specific surface area, higher porosity, and shorter ion and mass transport distance over normal 1D and 2D structures. In this review, the commonly used strategies for manufacturing 3D MXene architectures (3D MXenes and 3D MXene‐based composites) are summarized, such as template, assembly, 3D printing, and other methods. Special attention is also given to the structure–property relationships of 3D MXene architectures and their applications in electrochemical energy storage and conversion, including supercapacitors, rechargeable batteries, and electrocatalysis. Finally, the authors propose a brief perspective on future opportunities and challenges for 3D MXene architectures/devices.

Coreshell-like Ag2O/Ag2CO3 nanoheterostructures with tailored interface are fabricated by a facile, low-cost and one-step phase transformation method. The unique bandgap structure of the Ag2O/Ag2CO3 exhibits high separation efficiency of photogenerated electrons and holes, which effectively protects the Ag2CO3 semiconductor to avoid its photoreduction and gives rise to high activity and stability in degradation of the typical water pollutants. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Abstract The recent outbreak of a novel coronavirus SARS-CoV-2 (also known as 2019-nCoV) threatens global health, given serious cause for concern. SARS-CoV-2 is a human-to-human pathogen that caused fever, severe respiratory disease and pneumonia (known as COVID-19). By press time, more than 70,000 infected people had been confirmed worldwide. SARS-CoV-2 is very similar to the severe acute respiratory syndrome (SARS) coronavirus broke out 17 years ago. However, it has increased transmissibility as compared with the SARS-CoV, e.g. very often infected individuals without any symptoms could still transfer the virus to others. It is thus urgent to develop a rapid, accurate and onsite diagnosis methods in order to effectively identify these early infects, treat them on time and control the disease spreading. Here we developed an isothermal LAMP based method-iLACO (isothermal LAMP based method for COVID-19) to amplify a fragment of the ORF1ab gene using 6 primers. We assured the species-specificity of iLACO by comparing the sequences of 11 related viruses by BLAST (including 7 similar coronaviruses, 2 influenza viruses and 2 normal coronaviruses). The sensitivity is comparable to Taqman based qPCR detection method, detecting synthesized RNA equivalent to 10 copies of 2019-nCoV virus. Reaction time varied from 15-40 minutes, depending on the loading of virus in the collected samples. The accuracy, simplicity and versatility of the new developed method suggests that iLACO assays can be conveniently applied with for 2019-nCoV threat control, even in those cases where specialized molecular biology equipment is not available.

Mg and its alloys have been identified as promising bone implant materials owing to their natural degradability, good biocompatibility and favorable mechanical properties. Nevertheless, the too fast degradation rate usually results in a premature disintegration of mechanical integrity and local hydrogen accumulation, which limit their clinical bone repair application. In this work, the current research status regarding Mg bone implants was systematically reviewed. The relevant strategies to enhance the corrosion resistance, including purification, alloying treatment, surface coating and Mg-based metal matrix composite, are comprehensively discussed. The fabricating techniques for Mg bone implants are also presented. Particularly, laser additive manufacturing can fabricate customized shape and complex porous structure basing on its unique additive manufacturing concept. More importantly, it can achieve rapid heating and cooling due to the characteristics of high laser energy density and good controllability, thereby regulating the microstructure and performance. Furthermore, the current challenges and future research perspectives are put forward. This work aims to offer some meaningful guidelines for researchers on the future study of Mg bone implants.

The incorporation of hydroxyapatite (HAP) into poly-l-lactic acid (PLLA) matrix serving as bone scaffold is expected to exhibit bioactivity and osteoconductivity to those of the living bone. While too low degradation rate of HAP/PLLA scaffold hinders the activity because the embedded HAP in the PLLA matrix is difficult to contact and exchange ions with body fluid. In this study, biodegradable polymer poly (glycolic acid) (PGA) was blended into the HAP/PLLA scaffold fabricated by laser 3D printing to accelerate the degradation. The results indicated that the incorporation of PGA enhanced the degradation rate of scaffold as indicated by the weight loss increasing from 3.3% to 25.0% after immersion for 28 days, owing to the degradation of high hydrophilic PGA and the subsequent accelerated hydrolysis of PLLA chains. Moreover, a lot of pores produced by the degradation of the scaffold promoted the exposure of HAP from the matrix, which not only activated the deposition of bone like apatite on scaffold but also accelerated apatite growth. Cytocompatibility tests exhibited a good osteoblast adhesion, spreading and proliferation, suggesting the scaffold provided a suitable environment for cell cultivation. Furthermore, the scaffold displayed excellent bone defect repair capacity with the formation of abundant new bone tissue and blood vessel tissue, and both ends of defect region were bridged after 8 weeks of implantation.

Polyetheretherketone (PEEK)/β-tricalcium phosphate (β-TCP) scaffolds are expected to be able to combine the excellent mechanical strength of PEEK and the good bioactivity and biodegradability of β-TCP. While PEEK acts as a closed membrane in which β-TCP is completely wrapped after the melting/solidifying processing, the PEEK membrane degrades very little, hence the scaffolds cannot display bioactivity and biodegradability. The strategy reported here is to blend a biodegradable polymer with PEEK and β-TCP to fabricate multi-material scaffolds via selective laser sintering (SLS). The biodegradable polymer first degrades and leaves caverns on the closed membrane, and then the wrapped β-TCP is exposed to body fluid. In this study, poly(l-lactide) (PLLA) is adopted as the biodegradable polymer. The results show that large numbers of caverns form on the membrane with the degradation of PLLA, enabling direct contact between β-TCP and body fluid, and allowing for their ion-exchange. As a consequence, the scaffolds display the bioactivity, biodegradability and cytocompatibility. Moreover, bone defect repair studies reveal that new bone tissues grow from the margin towards the center of the scaffolds from the histological analysis. The bone defect region is completely connected to the host bone end after 8 weeks of implantation.

Two-dimensional conductive metal-organic frameworks (2D c-MOFs) as an emerging class of multifunctional materials have attracted extensive attention due to their predictable and diverse structures, intrinsic permanent porosity, high charge mobility, and excellent electrical conductivity. Such unique characteristics render them as a promising new platform for electrical related devices. This Minireview highlights the recent key progress of 2D c-MOFs with emphasis on the design strategies, unique electrical properties, and potential applications in electrochemical energy storage. The thorough elucidation of structure-function correlations may offer a guidance for the development of 2D c-MOFs based next-generation energy storage devices.

Abstract Quasi-solid-state Zn-air batteries are usually limited to relatively low-rate ability (<10 mA cm −2 ), which is caused in part by sluggish oxygen electrocatalysis and unstable electrochemical interfaces. Here we present a high-rate and robust quasi-solid-state Zn-air battery enabled by atomically dispersed cobalt sites anchored on wrinkled nitrogen doped graphene as the air cathode and a polyacrylamide organohydrogel electrolyte with its hydrogen-bond network modified by the addition of dimethyl sulfoxide. This design enables a cycling current density of 100 mA cm −2 over 50 h at 25 °C. A low-temperature cycling stability of over 300 h (at 0.5 mA cm −2 ) with over 90% capacity retention at −60 °C and a broad temperature adaptability (−60 to 60 °C) are also demonstrated.

Wearable electronics have received considerable attention in recent years. These devices have penetrated every aspect of our daily lives and stimulated interest in futuristic electronics. Thus, flexible batteries that can be bent or folded are desperately needed, and their electrochemical functions should be maintained stably under the deformation states, given the increasing demands for wearable electronics. Carbon nanomaterials, such as carbon nanotubes, graphene, and/or their composites, as flexible materials exhibit excellent properties that make them suitable for use in flexible batteries. Herein, the most recent progress on flexible batteries using carbon nanomaterials is discussed from the viewpoint of materials fabrication, structure design, and property optimization. Based on the current progress, the existing advantages, challenges, and prospects are outlined and highlighted.

Noble metal/semiconductor nanocomposites play an important role in high efficient photocatalysis. Herein, we demonstrate a facile strategy for fabrication of hollow Pt-ZnO nanocomposite microspheres with hierarchical structure under mild solvothermal conditions using Zn (CH(3)COO)(2)·2H(2)O and HPtCl(4) as the precursors, and polyethylene glycol-6000 (PEG-6000) and ethylene glycol as the reducing agent and solvent, respectively. The as-synthesized ZnO and Pt-ZnO composite nanocrystals were well characterized by powder X-ray diffraction (XRD), nitrogen-physical adsorption, scanning electron microscopy (SEM), energy dispersive X-ray (EDX), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), UV-vis diffuse reflectance spectra (DRS), and photoluminescence (PL) emission spectroscopy. It was found that Pt content greatly influences the morphology of Pt-ZnO composite nanocrystals. Suitable concentration of HPtCl(4) in the reaction solution system can produce well hierarchically hollow Pt-ZnO nanocomposite microspheres, which are composed of an assembly of fine Pt-ZnO nanocrystals. Photocatalytic tests of the Pt-ZnO microspheres for the degradation of the dye acid orange II revealed extremely high photocatalytic activity and stability compared with those of pure ZnO and corresponding Pt deposited ZnO. The remarkable photocatalytic performance of hollow Pt-ZnO microspheres mainly originated from their unique nanostructures and the low recombination rate of the e(-)/h(+) pairs by the platinum nanoparticles embedded in ZnO nanocrystals.

One of the most important research topics in smart grid technology is load forecasting, because accuracy of load forecasting highly influences reliability of the smart grid systems. In the past, load forecasting was obtained by traditional analysis techniques such as time series analysis and linear regression. Since the load forecast focuses on aggregated electricity consumption patterns, researchers have recently integrated deep learning approaches with machine learning techniques. In this study, an accurate deep neural network algorithm for short-term load forecasting (STLF) is introduced. The forecasting performance of proposed algorithm is compared with performances of five artificial intelligence algorithms that are commonly used in load forecasting. The Mean Absolute Percentage Error (MAPE) and Cumulative Variation of Root Mean Square Error (CV-RMSE) are used as accuracy evaluation indexes. The experiment results show that MAPE and CV-RMSE of proposed algorithm are 9.77% and 11.66%, respectively, displaying very high forecasting accuracy.

Abstract High‐capacity cathodes and anodes in energy storage area are required for delivering high energy density due to the ever‐increasing demands in the applications of electric vehicles and power grids, which suffer from significant safety concerns and poor cycling stability at the current stage. All‐solid‐state lithium batteries (ASSLBs) have been considered to be particularly promising within the new generation of energy storage, owing to the superiority of safety, wide potential window, and long cycling life. As the key component in ASSLBs, individual solid electrolytes that can meet practical application standards are very rare due to poor performance. To the present day, numerous research efforts have been expended to find applicable solid‐state electrolytes and tremendous progress has been achieved, especially for garnet‐type solid electrolytes. Nevertheless, the garnet‐type solid electrolyte is still facing some crucial dilemmas. Hence, the issues of garnet electrolytes' ionic conductivity, the interfaces between electrodes and garnet solid electrolytes, and application of theoretical calculation on garnet electrolytes are focuses in this review. Furthermore, prospective developments and alternative approaches to the issues are presented, with an aim to improve understanding of garnet electrolytes and promote their practical applications in solid‐state batteries.

In this study, a novel method that allows selective extraction of lithium and production of battery grade Li2CO3 is introduced, which includes nitration, selective roasting, water leaching and Li2CO3 preparation. By this method, metallic components in Li-ion battery waste are firstly transformed into corresponding nitrates, and then decomposed into insoluble oxides during roasting except for lithium nitrate, which is ready to be extracted by water leaching. Under the optimum conditions of nitration (70 °C, 5 h, acid-to-scrap ratio of 30 mmol/g), selective roasting (250 °C, 1 h) and 4-stage cross-current water leaching (25 °C, liquid-to-solid ratio of 2:1), lithium extraction up to 93% is achieved, whereas extraction of other metals like cobalt, nickel, copper etc. are <0.1%. The obtained lithium-rich solution (34.1 g/L lithium) is then subjected to a carbonation step at 95 °C for 30 min to form the desired Li2CO3. The purity of Li2CO3 produced is up to 99.95%, a level above the minimum standards required for battery grade Li2CO3. Application of this new process could significantly improve lithium recovery from waste Li-ion batteries, as the overall recovery of 90% for lithium achieved is much higher than previously reported lithium recovery of 60–80% from waste Li-ion batteries.

A tomato automatic detection method based on an improved YOLOv8s model is proposed to address the low automation level in tomato harvesting in agriculture. The proposed method provides technical support for the automatic harvesting and classification of tomatoes in agricultural production activities. The proposed method has three key components. Firstly, the depthwise separable convolution (DSConv) technique replaces the ordinary convolution, which reduces the computational complexity by generating a large number of feature maps with a small amount of calculation. Secondly, the dual-path attention gate module (DPAG) is designed to improve the model’s detection precision in complex environments by enhancing the network’s ability to distinguish between tomatoes and the background. Thirdly, the feature enhancement module (FEM) is added to highlight the target details, prevent the loss of effective features, and improve detection precision. We built, trained, and tested the tomato dataset, which included 3098 images and 3 classes. The proposed algorithm’s performance was evaluated by comparison with the SSD, faster R-CNN, YOLOv4, YOLOv5, and YOLOv7 algorithms. Precision, recall rate, and mAP (mean average precision) were used for evaluation. The test results show that the improved YOLOv8s network has a lower loss and 93.4% mAP on this dataset. This improvement is a 1.5% increase compared to before the improvement. The precision increased by 2%, and the recall rate increased by 0.8%. Moreover, the proposed algorithm significantly reduced the model size from 22 M to 16 M, while achieving a detection speed of 138.8 FPS, which satisfies the real-time detection requirement. The proposed method strikes a balance between model size and detection precision, enabling it to meet agriculture’s tomato detection requirements. The research model in this paper will provide technical support for a tomato picking robot to ensure the fast and accurate operation of the picking robot.

Abstract Herein, we successfully construct bifunctional electrocatalysts by synthesizing atomically dispersed Fe−Se atom pairs supported on N‐doped carbon (Fe−Se/NC). The obtained Fe−Se/NC shows a noteworthy bifunctional oxygen catalytic performance with a low potential difference of 0.698 V, far superior to that of reported Fe‐based single‐atom catalysts. The theoretical calculations reveal that p‐d orbital hybridization around the Fe−Se atom pairs leads to remarkably asymmetrical polarized charge distributions. Fe−Se/NC based solid‐state rechargeable Zn‐air batteries (ZABs−Fe−Se/NC) present stable charge/discharge of 200 h (1090 cycles) at 20 mA cm −2 at 25 °C, which is 6.9 times of ZABs−Pt/C+Ir/C. At extremely low temperature of −40 °C, ZABs−Fe−Se/NC displays an ultra‐robust cycling performance of 741 h (4041 cycles) at 1 mA cm −2 , which is about 11.7 times of ZABs−Pt/C+Ir/C. More importantly, ZABs−Fe−Se/NC could be operated for 133 h (725 cycles) even at 5 mA cm −2 at −40 °C.

-based metal-organic framework (MOF) with a rare tcj topology has been solvothermally synthesized and displays relatively good thermal and chemical stabilities. Interestingly, the MOF can sensitively and selectively sense acetylacetone (acac) via a fluorescence enhancement effect with a detection limit of 0.10 ppm and good reusability, which demonstrates the first example of a MOF-based turn-on fluorescent sensor for acac.